import cv2
import numpy as np

def compute_tracks(matches):
    """计算对应点的轨迹（Tracks）"""
    tracks = []
    for match in matches:
        track = (match.queryIdx, match.trainIdx)
        tracks.append(track)
    return tracks

def compute_connected_graph(matches, threshold=10):
    """计算连通图 G（结点代表图片，边代表其之间有足够的匹配点）"""
    graph = {}
    for i, match in enumerate(matches):
        if len(match) >= threshold:
            graph[i] = match
    return graph

def select_edge(graph):
    """在 G 中选取一条边 e"""
    return next(iter(graph.items()))

def robust_estimate_essential_matrix(kp1, kp2, matches, K):
    """鲁棒估计 e 所对应的本质矩阵 E"""
    pts1 = np.float32([kp1[m.queryIdx].pt for m in matches])
    pts2 = np.float32([kp2[m.trainIdx].pt for m in matches])
    E, mask = cv2.findEssentialMat(pts1, pts2, K, method=cv2.RANSAC, prob=0.999, threshold=1.0)
    return E, mask

def decompose_essential_matrix(E, K, pts1, pts2):
    """分解本质矩阵 E，得到两张图片摄像机的位姿（即外参数）"""
    _, R, t, _ = cv2.recoverPose(E, pts1, pts2, K)
    return R, t

def triangulate_points(K, R, t, pts1, pts2):
    """三角化 t ∩ e 的点，作为初始的重建结果"""
    proj1 = np.hstack((np.eye(3), np.zeros((3, 1))))
    proj2 = np.hstack((R, t))
    proj1 = K @ proj1
    proj2 = K @ proj2
    points_4d = cv2.triangulatePoints(proj1, proj2, pts1.T, pts2.T)
    points_3d = points_4d[:3] / points_4d[3]
    return points_3d.T

def pnp_estimate_pose(K, points_3d, points_2d):
    """用PnP方法估计摄像机位姿（外参数）"""
    _, rvec, tvec = cv2.solvePnP(points_3d, points_2d, K, None)
    R, _ = cv2.Rodrigues(rvec)
    return R, tvec

def bundle_adjustment():
    """执行 Bundle Adjustment（此处省略具体实现）"""
    pass

def incremental_sfm(K, keypoints, matches):
    """主函数：增量法SFM系统的求解步骤"""
    tracks = compute_tracks(matches)
    graph = compute_connected_graph(matches)
    
    while graph:
        edge, match = select_edge(graph)
        kp1, kp2 = keypoints[edge[0]], keypoints[edge[1]]
        
        E, mask = robust_estimate_essential_matrix(kp1, kp2, match, K)
        pts1 = np.float32([kp1[m.queryIdx].pt for m in match])
        pts2 = np.float32([kp2[m.trainIdx].pt for m in match])
        
        R, t = decompose_essential_matrix(E, K, pts1, pts2)
        points_3d = triangulate_points(K, R, t, pts1, pts2)
        
        del graph[edge]
        
        if graph:
            edge, match = select_edge(graph)
            kp1, kp2 = keypoints[edge[0]], keypoints[edge[1]]
            pts1 = np.float32([kp1[m.queryIdx].pt for m in match])
            pts2 = np.float32([kp2[m.trainIdx].pt for m in match])
            
            R, t = pnp_estimate_pose(K, points_3d, pts2)
            new_points_3d = triangulate_points(K, R, t, pts1, pts2)
            
            points_3d = np.vstack((points_3d, new_points_3d))
            del graph[edge]
            
            bundle_adjustment()
    
    return points_3d, R, t

# 示例用法
K = np.array([[fx, 0, cx],
              [0, fy, cy],
              [0, 0, 1]])

# 假设 keypoints 和 matches 已经从图像集中提取并计算好
keypoints = [...]  # 包含每张图片的特征点
matches = [...]    # 包含每对图片之间的匹配结果

points_3d, R, t = incremental_sfm(K, keypoints, matches)

print(f"三维点云: {points_3d}")
print(f"摄像机位姿: R = {R}, t = {t}")